jobs_gender <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-03-05/jobs_gender.csv")
## Parsed with column specification:
## cols(
## year = col_double(),
## occupation = col_character(),
## major_category = col_character(),
## minor_category = col_character(),
## total_workers = col_double(),
## workers_male = col_double(),
## workers_female = col_double(),
## percent_female = col_double(),
## total_earnings = col_double(),
## total_earnings_male = col_double(),
## total_earnings_female = col_double(),
## wage_percent_of_male = col_double()
## )
earnings_female <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-03-05/earnings_female.csv")
## Parsed with column specification:
## cols(
## Year = col_double(),
## group = col_character(),
## percent = col_double()
## )
employed_gender <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-03-05/employed_gender.csv")
## Parsed with column specification:
## cols(
## year = col_double(),
## total_full_time = col_double(),
## total_part_time = col_double(),
## full_time_female = col_double(),
## part_time_female = col_double(),
## full_time_male = col_double(),
## part_time_male = col_double()
## )